> ## Documentation Index
> Fetch the complete documentation index at: https://developer.audioshake.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Diarization

> Label who spoke when in a recording, with speaker count and confidence scores

Run diarization to get who-spoke-when segments, the number of speakers, and confidence scores — without separating the audio. It returns the same diarization JSON that [Multi-Speaker Separation](https://developer.audioshake.ai/multi-speaker-separation.md) includes alongside its stems, so use `multi_voice` instead when you also need the per-speaker audio.

## Create a Task

```python diarization.py theme={null}
import requests

API_KEY = "your_api_key"
HEADERS = {"Content-Type": "application/json", "x-api-key": API_KEY}

response = requests.post(
    "https://api.audioshake.ai/tasks",
    headers=HEADERS,
    json={
        "assetId": "your_asset_id",
        "targets": [
            {"model": "diarization", "formats": ["json"]}
        ]
    }
)

task_id = response.json()["id"]
print(f"Task created: {task_id}")
```

```javascript diarization.js theme={null}
const API_KEY = "your_api_key";
const headers = { "Content-Type": "application/json", "x-api-key": API_KEY };

const createRes = await fetch("https://api.audioshake.ai/tasks", {
  method: "POST",
  headers,
  body: JSON.stringify({
    assetId: "your_asset_id",
    targets: [
      { model: "diarization", formats: ["json"] }
    ]
  })
});

const { id: taskId } = await createRes.json();
console.log(`Task created: ${taskId}`);
```

```bash curl theme={null}
curl -X POST "https://api.audioshake.ai/tasks" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $AUDIOSHAKE_API_KEY" \
  -d '{
    "assetId": "your_asset_id",
    "targets": [
      { "model": "diarization", "formats": ["json"] }
    ]
  }'
```

[Check Task status](https://developer.audioshake.ai/check-task-status.md) to monitor progress and download results, or use [webhooks](https://developer.audioshake.ai/api-reference/tasks/webhooks.md) to be notified when the target completes.

## Output

The task returns one `diarization` JSON output:

```json diarization.json theme={null}
{
  "results": {
    "diarization": {
      "segments": [
        { "speaker": "SPEAKER_01", "start": 0.213, "end": 4.673 },
        { "speaker": "SPEAKER_00", "start": 4.813, "end": 12.793 },
        { "speaker": "SPEAKER_01", "start": 11.913, "end": 19.993 }
      ],
      "num_speakers": 2,
      "scores": {
        "scores_per_frame": {
          "assignment_confidence": { "resolution": 0.02, "score": [0.883, 0.946, "..."] }
        },
        "scores": {
          "assignment_confidence": 0.946
        }
      }
    }
  }
}
```

All fields are relative to `results.diarization`:

* `segments` — one entry per speech segment, with `speaker` and `start`/`end` in seconds. The timeline is overlap-aware: segments from different speakers can overlap when people talk at once.
* `num_speakers` — number of distinct speakers detected.
* `scores` — `assignment_confidence` in \[0, 1], per-frame at 20 ms resolution and for the whole file: how confident the model is that speech is attributed to the right speaker.

## Use cases

* Attribute transcripts to speakers without paying for audio separation
* Count speakers and measure talk time in meetings, calls, and interviews
* Pre-screen recordings to decide which need full Multi-Speaker Separation

> [card] [Multi-Speaker Separation](https://developer.audioshake.ai/multi-speaker-separation.md)
>
> Get per-speaker audio stems along with the same diarization output.

> [card] [Speech Recovery](https://developer.audioshake.ai/speech-recovery.md)
>
> Denoise and de-reverb speech recordings.


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